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ShipFluxby eTechCube

AI

Intelligence that always degrades to rules

Courier recommendation, delay and EDD prediction, address scoring and network fraud — powered by the courier-performance data only a multi-tenant platform accumulates. Every model falls back to a transparent rules baseline, so you're never at the mercy of a black box.

100%models fall back to rules

Try it live

Courier recommendation, explained

Describe a shipment. The engine ranks carriers on delivery speed, cost and COD-return risk — and when you flip to the rules baseline, you still get a sensible answer. That's the whole promise: AI that degrades to rules.

500 g
Engine

Success-adjusted ranking with a confidence score and explanation.

Recommended · COD

Xpressbees

Fastest to this zone

55%

confidence

3 days

Est. delivery

₹40

All-in rate

11%

RTO risk

  • 1
    Xpressbees3d · ₹40 · 11% risk
  • 2
    Delhivery3d · ₹43 · 11% risk
  • 3
    Ecom Express3d · ₹42 · 11% risk
  • 4
    DTDC3d · ₹39 · 12% risk
  • 5
    Blue Dart3d · ₹68 · 11% risk

Illustrative simulation of the recommendation engine — not live carrier scores.

The problem today

Courier ranking biased by the aggregator's margin, static delivery promises, bad addresses causing RTOs, and no cross-merchant fraud signal.

How ShipFlux solves it

Explainable courier recommendations ('why this courier'), data-driven EDD per lane × courier, address validation at intake, and privacy-aware network fraud signals shared across tenants — each with a rules fallback and a kill-switch.

Explainable reco

'Why this courier' from lane performance and your own history — no pay-to-rank.

Data-driven EDD

Delivery dates computed from real events, embeddable at checkout.

Address intelligence

Validate, standardize and score addresses before the label prints.

Network fraud

Hashed, privacy-aware risk signals shared across all tenants.

How it works

1

Capture

Every shipment and tracking event feeds a courier-performance graph.

2

Learn

Models score couriers, delays, addresses and fraud per lane and pincode.

3

Explain

You see why — lane performance, your history — never a black box.

4

Fall back

If a model is off or unsure, a transparent rules baseline takes over.

A worked example

On a lane where one courier delivers 96% first-attempt and another 82%, the recommendation shows the gap and the reason — so you pick on outcomes, not on who paid for placement.

ShipFlux vs ClickPost

Full comparison →
CapabilityShipFluxClickPost
Self-serve, no sales call, no minimum volume
Autonomous NDR outreach (WhatsApp/IVR)

Common questions

Every service degrades to a transparent rules baseline, with a kill-switch — you always get a usable answer.

Never. Recommendations are explainable and based on lane performance and your own outcomes.

Ship smarter, with a safety net

Start free — the AI helps, the rules protect.

Free to start · no credit card · no lock-in · export & cancel anytime